README

Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance. Smile is well documented and please check out the project website for programming guides and more information.

For Clojure API, add the following dependency to your project or build file:

    [org.clojars.haifengl/smile "2.6.0"]

Some algorithms rely on BLAS and LAPACK (e.g. manifold learning, some clustering algorithms, Gaussian Process regression, MLP, etc). To use these algorithms, you should include OpenBLAS for optimized matrix computation:

    [org.bytedeco/arpack-ng "3.7.0-1.5.4"]
    [org.bytedeco/openblas-platform "0.3.10-1.5.4"]
    [org.bytedeco/arpack-ng          "3.7.0-1.5.4"]
    [org.bytedeco/arpack-ng-platform "3.7.0-1.5.4"]
    [org.bytedeco/openblas "0.3.10-1.5.4"]
    [org.bytedeco/javacpp "1.5.4"]

If you prefer other BLAS implementations, you can use any library found on the “java.library.path” or on the class path, by specifying it with the “org.bytedeco.openblas.load” system property. For example, to use the BLAS library from the Accelerate framework on Mac OS X, we can pass options such as -Djava.library.path=/usr/lib/ -Dorg.bytedeco.openblas.load=blas.

For a default installation of MKL that would be -Dorg.bytedeco.openblas.load=mkl_rt. Or you may simply include smile-mkl module in your project, which includes MKL binaries. With smile-mkl module in the class path, Smile will automatically switch to MKL.

    [org.clojars.haifengl/smile-mkl "2.6.0"]

Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.